A Real Time Cardiomyopathy Detection Tool Using Ml Ensemble Models

Left Ventricular noncompaction (LVNC) is a recently classified form of cardiomyopathy. Although various methods have been proposed for accurately quantifying trabeculae in the left ventricle (LV), consensus on the optimal approach remains elusive. Previous research introduced DL-LVTQ, a deep learning solution for trabecular quantification based on a UNet 2D convolutional neural network (CNN) architecture and a graphical user interface (GUI) to streamline its use in clinical workflows. Building on this foundation, this work presents LVNC detector, an enhanced application designed to support cardiologists in the automated diagnosis of LVNC. The application integrates two segmentation models: DL-LVTQ and ViTUNet, the latter inspired by modern hybrid architectures combining convolutional neural networks (CNNs) and transformer-based designs. These models, implemented within an ensemble framework, leverage advancements in deep learning to improve the accuracy and robustness of magnetic resonance imaging (MRI) segmentation. Key innovations include multithreading to optimize model loading times and ensemble methods to enhance segmentation consistency across MRI slices. Additionally, the platform-independent design ensures compatibility with Windows and Linux, eliminating complex setup requirements. The LVNC detector delivers an efficient and user-friendly solution for LVNC diagnosis. It enables real-time performance and allows cardiologists to select and compare segmentation models for improved diagnostic outcomes. This work demonstrates how state-of-the-art machine learning techniques can seamlessly integrate into clinical practice to reduce human error and expedite diagnostic processes.